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Record W4408488902 · doi:10.1136/openhrt-2024-002866

Investigating the impact of multidimensional sleep traits on cardiovascular diseases and the mediating role of depression

2025· article· en· W4408488902 on OpenAlexaff
Hao Zhao, Xiaojie Wang, Lan Guo, Xiuwen Li, Kayla M. Teopiz, Roger S. McIntyre, Wanxin Wang, Ciyong Lu

Bibliographic record

VenueOpen Heart · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsMedicineChronotypeInternal medicineInsomniaDepression (economics)Excessive daytime sleepinessMyocardial infarctionMajor depressive disorderObstructive sleep apneaCoronary artery diseaseCardiologyAtrial fibrillationSleep apneaSleep disorderPsychiatryCircadian rhythm

Abstract

fetched live from OpenAlex

BACKGROUND: Observational studies have reported that sleep is associated with the risk of major depressive disorder (MDD) and cardiovascular diseases (CVDs). However, the causal relationships among various sleep traits remain contentious, and whether MDD mediates the impact of specific sleep traits on CVDs is unclear. METHODS: We performed two-sample Mendelian randomisation analyses to explore whether insomnia, sleep time, daytime napping, daytime sleepiness, chronotype, snoring or obstructive sleep apnoea were causally associated with the risk of five CVDs, including coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), atrial fibrillation and stroke. Mediation analyses were performed to assess the proportion mediated by MDD. RESULTS: Genetically predicted insomnia, short sleep, daytime napping and daytime sleepiness increased the risk of CVDs, with the OR ranging from 1.24 (95% CI 1.06 to 1.45) for insomnia on stroke to 1.55 (95% CI 1.28 to 1.89) for insomnia on MI. In contrast to short sleep, genetically predicted sleep duration decreased the risk of CAD (OR 0.88 (95% CI 0.80 to 0.97)), MI (OR 0.89 (95% CI 0.80 to 0.99)) and HF (OR 0.90 (95% CI 0.83 to 0.98)). However, we found no significant associations of long sleep, chronotype, snoring and obstructive sleep apnoea with increased risk for any CVD subtype. Additionally, the effect of insomnia was partially mediated by MDD for the risk of CAD (proportion mediated: 8.81% (95% CI 1.20% to 16.43%)), MI (9.17% (95% CI 1.71% to 16.63%)) and HF (14.46% (95% CI 3.48% to 25.45%)). Similarly, the effect of short sleep was partially mediated by MDD for the risk of CAD (8.92% (95% CI 0.87% to 16.97%)), MI (11.43% (95% CI 0.28% to 22.57%)) and HF (12.65% (95% CI 1.35% to 23.96%)). MDD also partially mediated the causal effects of insomnia on stroke, sleep duration on CAD, MI and HF, daytime napping on HF and daytime sleepiness on CAD. CONCLUSIONS: Our study provides evidence that genetically predicted insomnia, short sleep, frequent daytime napping and sleepiness are associated with a higher risk of certain CVD subtypes, partly mediated by MDD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.310
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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